q-BioGroup

Background

C. difficile can utilize heme from the host to protect against antibiotic therapy and immune cell mediated oxidative stress produced at the host-pathogen interface. HatRT system in C. difficile senses and detoxifies excess intracellular heme through efflux. The results herein further refine our current model of heme homeostasis in C. difficile, Figure 1.

Fig. 1. C. difficile senses and hijacks host heme for incorporation into an oxidative stress defense system. This effect would presumably be related to the ability of heme to detoxify the nitro-radicals generated by metronidazole activation

HsmR senses low concentrations of heme and activates expression of the hsmRA operon which leads to the integration of heme into HsmA. Heme-bound HsmA within the membrane shields the bacterium against redox active molecules. Concurrently, HatR binds heme, derepressing the hatRT operon, leading to subsequent efflux of free heme through HatT, and resulting in a relief from heme toxicity. Together these systems function to maintain a tolerable concentration of intracellular heme for C. difficile to protect itself against the stressors encountered within the host during CDI [2].

Adding target reactions to the model

Starting from the published C. difficile genome-scale metabolic networks CD196 We inserted heam transport reactions as reversible (while originally C. bifermentans metabolic model they were compiled as irreversible). Referencing to the script Building_Model.m. It will apply the method addReactionGEM_Unito.m.

CD196 -> CD196HemeEXs

Specifying reactions:

  • EX_pheme(e) : [e] : pheme <==>
  • HEMEti : atp[c] + h2o[c] + pheme[e] <==> adp[c] + h[c] + pheme[c] + pi[c]

Checking out for reactions included in this model.

ReactionIndex ReactionName ReactionBIGG ReactionEQ Lb Ub
1369 Heme transport via ABC system HEMEti h2o[c] + atp[c] + pheme[e] <==> h[c] + pi[c] + adp[c] + pheme[c] -1000 1000
1370 exchange reaction for heme EX_pheme(e) [e] : pheme <==> -1000 1000
645 Ferrochelatase, cytosol FCLTc [c] : fe2 + ppp9 –> (2) h + pheme 0 1000
1368 biomass205 biomass205 [c] : (0.0079397) nad + (0.0079397) nadp + (37.16) h2o + (0.26101) glu_L + (0.0079397) ACP + (0.0079397) coa + (0.0079397) fad + (0.40933) gly + (41.2914) atp + (0.042403) gtp + (0.0079397) thmpp + (0.26101) gln_L + (0.013437) dttp + (0.026124) ctp + (0.013437) datp + (0.013437) dctp + (0.013437) dgtp + (0.0079397) amet + (0.0079397) adocbl + (0.14833) asp_L + (0.26756) ala_L + (0.0079397) ptrc + (0.0079397) 10fthf + (0.0079397) thf + (0.081948) his_L + (0.34746) leu_L + (0.18684) thr_L + (0.0079397) 2dmmq8 + (0.0079397) mqn8 + (0.056954) cys_L + (0.1934) arg_L + (0.14833) asn_L + (0.1135) met_L + (0.0079397) ca2 + (0.0085871) pg180 + (0.0085871) clpn180 + (0.0085871) pgai17 + (0.0085871) clpnai17 + (0.0085871) pgi17 + (0.0085871) clpni17 + (0.0079397) cobalt2 + (0.028225) utp + (0.0079397) cu2 + (0.3237) lys_L + (0.0079397) q8 + (0.0079397) fe2 + (0.0079397) pheme + (0.0079397) fe3 + (0.0079397) gthrd + (0.21675) ser_L + (0.17619) phe_L + (0.16103) pro_L + (0.11104) tyr_L + (0.0079397) k + (0.27043) ile_L + (0.0018061) udcpdp + (0.0079397) 5mthf + (0.0079397) mg2 + (0.0079397) mn2 + (0.0079397) pydx5p + (0.0018061) PGP + (0.0085871) pe180 + (0.0085871) peai17 + (0.0085871) pei17 + (0.0079397) ribflv + (0.0079397) sheme + (0.0079397) so4 + (0.0079397) spmd + (0.0018061) sttca1 + (0.0018061) ai17tca1 + (0.0018061) i17tca1 + (0.0018061) sttcaglc + (0.0018061) ai17tcaglc + (0.0018061) i17tcaglc + (0.0018061) sttcaala_D + (0.0018061) ai17tcaala_D + (0.0018061) i17tcaala_D + (0.0018061) tcam + (0.0018061) sttcaacgam + (0.0018061) ai17tcaacgam + (0.0018061) i17tcaacgam + (0.0018061) glyc45tcaala_D + (0.0018061) glyc45tcaglc + (0.0018061) glyc45tca + (0.054496) trp_L + (0.30772) val_L + (0.0079397) zn2 + dnarep + proteinsynth + (0.0079397) cl + rnatrans –> (41.257) h + (41.2491) pi + (0.18489) ppi + (41.257) adp + (0.0079397) apoACP + (0.0079397) cbi + biomass + (0.0079397) dmbzid + (0.0072242) PGPm1 0 1000

CD196 -> CD196HemeSink

We edit the original published model CD196 to include the reversible sink reaction for heme. This sink reaction is of great use for those compounds produced or consumed by non-metabolic cellular processes (see Appendix A: Looking for model boundary reactions for more details about sink reactions and the other so called boundary reactions and thier implications on the final model). We also edit the original model deleting those reactions involving extracellular heme. However, these reactions are not permanently lost since they are added later to the final model through the Petri Net formalism.

Specyfing reactions:

  • sink_pheme(c) : [c] : pheme <==>

Checking out for reactions included in this model.

ReactionIndex ReactionName ReactionBIGG ReactionEQ Lb Ub
645 Ferrochelatase, cytosol FCLTc [c] : fe2 + ppp9 –> (2) h + pheme 0 1000
1368 biomass205 biomass205 [c] : (0.0079397) nad + (0.0079397) nadp + (37.16) h2o + (0.26101) glu_L + (0.0079397) ACP + (0.0079397) coa + (0.0079397) fad + (0.40933) gly + (41.2914) atp + (0.042403) gtp + (0.0079397) thmpp + (0.26101) gln_L + (0.013437) dttp + (0.026124) ctp + (0.013437) datp + (0.013437) dctp + (0.013437) dgtp + (0.0079397) amet + (0.0079397) adocbl + (0.14833) asp_L + (0.26756) ala_L + (0.0079397) ptrc + (0.0079397) 10fthf + (0.0079397) thf + (0.081948) his_L + (0.34746) leu_L + (0.18684) thr_L + (0.0079397) 2dmmq8 + (0.0079397) mqn8 + (0.056954) cys_L + (0.1934) arg_L + (0.14833) asn_L + (0.1135) met_L + (0.0079397) ca2 + (0.0085871) pg180 + (0.0085871) clpn180 + (0.0085871) pgai17 + (0.0085871) clpnai17 + (0.0085871) pgi17 + (0.0085871) clpni17 + (0.0079397) cobalt2 + (0.028225) utp + (0.0079397) cu2 + (0.3237) lys_L + (0.0079397) q8 + (0.0079397) fe2 + (0.0079397) pheme + (0.0079397) fe3 + (0.0079397) gthrd + (0.21675) ser_L + (0.17619) phe_L + (0.16103) pro_L + (0.11104) tyr_L + (0.0079397) k + (0.27043) ile_L + (0.0018061) udcpdp + (0.0079397) 5mthf + (0.0079397) mg2 + (0.0079397) mn2 + (0.0079397) pydx5p + (0.0018061) PGP + (0.0085871) pe180 + (0.0085871) peai17 + (0.0085871) pei17 + (0.0079397) ribflv + (0.0079397) sheme + (0.0079397) so4 + (0.0079397) spmd + (0.0018061) sttca1 + (0.0018061) ai17tca1 + (0.0018061) i17tca1 + (0.0018061) sttcaglc + (0.0018061) ai17tcaglc + (0.0018061) i17tcaglc + (0.0018061) sttcaala_D + (0.0018061) ai17tcaala_D + (0.0018061) i17tcaala_D + (0.0018061) tcam + (0.0018061) sttcaacgam + (0.0018061) ai17tcaacgam + (0.0018061) i17tcaacgam + (0.0018061) glyc45tcaala_D + (0.0018061) glyc45tcaglc + (0.0018061) glyc45tca + (0.054496) trp_L + (0.30772) val_L + (0.0079397) zn2 + dnarep + proteinsynth + (0.0079397) cl + rnatrans –> (41.257) h + (41.2491) pi + (0.18489) ppi + (41.257) adp + (0.0079397) apoACP + (0.0079397) cbi + biomass + (0.0079397) dmbzid + (0.0072242) PGPm1 0 1000
1369 sink reaction for heme sink_pheme(c) [c] : pheme <==> -1000 1000

The CDI infection model

The CDI infection Petri Net model is composed by places (graphically represented by circles) corresponding to epithelial cells, bacterial biomass, metabolites and tissue state (i.e. damage to the colonic mucosa) and by transitions (graphically represented by rectangles) corresponding to the interactions among the entities, cellular death, intake or efflux of metabolites, toxin action, intestinal inflammation and drug activity.

The model is composed by five modules: IECs, LumenEnv, FbaEnv, Drug, BloodVessel.

Fig. 3. Caption Figure 3

Testing CD196SinkHeme model alone

Lets first compile the metabolic model from the .RData storing the chemical reaction network, and then generate a flux distribution to test the optimization of the mathematical programming object.

We exploited flux_balance() method to perform sensitivity analysis acting on the permissible flux through target reactions (flux distribution is obtained through running parsimonious FBA or minimum total flux (MTF)).

We also exploited flux_balance_fba() method to perform sensitivity analysis acting on the permissible flux through target reactions (flux distribution is obtained through running FBA).

Also, we evaluate the estimanted flux solving LPs through GLPK called within epimod framework.

FBA solution space is explored by Boundary Reactions bounds variation.

Apparently reactions “EX_leu_L(e)” and “EX_pro_L(e)” and “EX_val_L(e)” showed unexpected behaviour. EX_pro_L(e) is always 0 whereas EX_leu_L(e) is always positive. Therefore, we are going to expore the solution ladscape for “EX_leu_L(e)” and “EX_pro_L(e)” “EX_val_L(e)” reactions flux estimations.

Testing CD196SinkHeme and diet

CD196SinkHeme genome-scale metabolic model is considered in the final model exposed to a defined metabolic environment. The metabolomic landscape available to bacteria is given by a diet formulation. Therefore, from the bacteria perspective, the dietary input compartment represents the exchange medium consisting of all the dietary ingredients that C. difficile can consume. This step focuses on adjusting the exchange reactions linked to the metabolite identifiers.

diets.RData is generated following the Script4Diets.R. Starting from diet data download from the repository of Virtual Metabolic Human, we then convert fluxed expressed as [flux in mmol/human day] to [flux in mmol/human h]. We assumed that this flux is considered as metabolites concentrations as steady state [mmol], i.e. the maximal uptake can be constrained by restricting the lower bounds.

We exploited flux_balance_fba() method to perform sensitivity analysis acting on the permissible flux through target reactions (flux distribution is obtained through running FBA).

We will obtain fluxes solutions considering a medium metabolites concentrations equal to the lower bounds contraints of the published model. Solution are estimated given EX_biomass(e) reactions’s upper bounds equal that of the published model. Lower bounds sets associated to metaboliutes not envised in the diet formulation are kept unvaried.

Based on amino acid utilization, the total consumption of nitrogen is between the interval of 10 [mmol/gDW h] and 0.1 [mmol/gDW h]

HYD4 flux distribution

HYD4 flux distribution solutions produced from rnorm bounds distribution (n = 5000)

Appendix A: Looking for model boundary reactions

There are three different types of pre-defined boundary reactions:

Exchange. An exchange reaction is a reversible reaction that adds to or removes an extracellular metabolite from the extracellular compartment.

Example of exchange reactions:

ReactionIndex ReactionBIGG ReactionEQ Lb Ub
468 EX_12dgr180(e) [e] : 12dgr180 <==> -1000 1000
469 EX_13ppd(e) [e] : 13ppd <==> -1000 1000
470 EX_2ddglcn(e) [e] : 2ddglcn <==> -1000 1000
471 EX_2mbut(e) [e] : 2mbut <==> -1000 1000
472 EX_34dhpha(e) [e] : 34dhpha <==> -1000 1000
473 EX_4abut(e) [e] : 4abut <==> -1000 1000
474 EX_4hphac(e) [e] : 4hphac <==> -1000 1000
475 EX_4mcat(e) [e] : 4mcat <==> -1000 1000

Demand. A demand reaction is an irreversible reaction that consumes an intracellular metabolite.

Example of demand reactions:

ReactionIndex ReactionBIGG ReactionEQ Lb Ub
412 DM_4HBA [c] : 4hba –> 0 1000
413 DM_5DRIB [c] : 5drib –> 0 1000
414 DM_5MTR [c] : 5mtr –> 0 1000
415 DM_GCALD [c] : gcald –> 0 1000
416 DM_HQN [c] : hqn –> 0 1000
418 DM_btn [c] : btn –> 0 1000
419 DM_clpn140(c) [c] : clpn140 –> 0 1000
420 DM_clpn160(c) [c] : clpn160 –> 0 1000
421 DM_clpn180(c) [c] : clpn180 –> 0 1000
422 DM_clpnai15(c) [c] : clpnai15 –> 0 1000
423 DM_clpnai17(c) [c] : clpnai17 –> 0 1000
424 DM_clpni15(c) [c] : clpni15 –> 0 1000
425 DM_clpni16(c) [c] : clpni16 –> 0 1000
426 DM_clpni17(c) [c] : clpni17 –> 0 1000
427 DM_dad_5 [c] : dad_5 –> 0 1000
428 DM_dhptd(c) [c] : dhptd <==> -1000 1000
429 DM_teich_45_BS(c) [c] : teich_45_BS –> 0 1000

Sink. A sink is similar to an exchange but specifically for intracellular metabolites, i.e., a reversible reaction that adds or removes an intracellular metabolite.

Example of demand reactions:

ReactionIndex ReactionBIGG ReactionEQ Lb Ub
1365 sink_PGPm1[c] [c] : PGPm1 <==> -1000 1000
1366 sink_dmbzid [c] : dmbzid <==> -1000 1000
1367 sink_gthrd(c) [c] : gthrd <==> -1000 1000
1369 sink_pheme(c) [c] : pheme <==> -1000 1000

All of them are unbalanced pseudo reactions, that means they fulfill a function for modeling by adding to or removing metabolites from the model system but are not based on real biology.

Fig. 2. Caption Figure 2

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